Reinforcement learning (RL) has rapidly become a core stage of modern foundation-model development. While large-scale pretraining remains essential, today's most capable models rely heavily on post-training techniques to improve reasoning, tool use, and multi-turn interaction. These workflows depend on scalable reinforcement learning infrastructure capable of running across multi-node GPU clusters.
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